ArticleG3 (Bethesda, Md.)2020
Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum.
Article in G3 (Bethesda, Md.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
20 citing papers in PubMed.
- Improving multi-trait genomic prediction using synthetic traits from hyperspectral data based on co-heritability.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Small Signaling Peptides inPlant direct · 2026Article
- High-Throughput Screen of NPQ in Sorghum Shows Highly Polygenic Architecture of Photoprotection.Plant-environment interactions (Hoboken, N.J.) · 2026Article
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- Candidate genes for anthracnose resistance in Senegalese sorghum: a machine learning-based exploration.Functional & integrative genomics · 2025Article
- Stage-resolved gene regulatory network analysis reveals developmental reprogramming and genes with robust stem-preferred expression in sorghum.BMC plant biology · 2025Article
- Crop growth model-enabled genetic mapping of biomass accumulation dynamics in photoperiod-sensitive sorghum.The plant genome · 2025Article
- Constitutive down-regulation of liguleless alleles in sorghum drives increased productivity and water use efficiency.Plant biotechnology journal · 2025Article
- Bioenergy sorghum stem density increases threefold following internode elongation due to continued accumulation of lignified cell walls and complex regulation of genes involved in cell wall biosynthesis.Biotechnology for biofuels and bioproducts · 2025Article
- Genomic and phenomic prediction for soybean seed yield, protein, and oil.The plant genome · 2025Article
- Improving the accuracy of genomic prediction in dairy cattle using the biologically annotated neural networks framework.Journal of animal science and biotechnology · 2024Article
- Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain.The plant genome · 2023Article
- Use of genomic prediction to screen sorghum B-lines in hybrid testcrosses.The plant genome · 2023Article
- Genome-wide marker effect heterogeneity is associated with a large effect dormancy locus in winter malting barley.The plant genome · 2022Article
- Leveraging probability concepts for cultivar recommendation in multi-environment trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions.Plant physiology · 2021Article
- Assessing combining abilities, genomic data, and genotype × environment interactions to predict hybrid grain sorghum performance.The plant genome · 2021Article
- Can improved canopy light transmission ameliorate loss of photosynthetic efficiency in the shade? An investigation of natural variation in Sorghum bicolor.Journal of experimental botany · 2021Article
- Identification of Candidate Forage Yield Genes in Sorghum (Frontiers in plant science · 2021Article
- High-Throughput Phenotyping (HTP) Data Reveal Dosage Effect at Growth Stages inPlants (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ability to connect genetic information between traits over time allow Bayesian networks to offer a powerful probabilistic framework to construct genomic prediction models. In this study, we phenotyped a diversity panel of 869 biomass sorghum (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.